Auditory Cortical Changes Precede Brainstem Changes During Rapid Implicit Learning: Evidence From Human EEG

The auditory system is sensitive to stimulus regularities such as frequently occurring sounds and sound combinations. Evidence of regularity detection can be seen in how neurons across the auditory network, from brainstem to cortex, respond to the statistical properties of the soundscape, and in the rapid learning of recurring patterns in their environment by children and adults. Although rapid auditory learning is presumed to involve functional changes to the auditory network, the chronology and directionality of changes are not well understood. To study the mechanisms by which this learning occurs, auditory brainstem and cortical activity was simultaneously recorded via electroencephalogram (EEG) while young adults listened to novel sound streams containing recurring patterns. Neurophysiological responses were compared between easier and harder learning conditions. Collectively, the behavioral and neurophysiological findings suggest that cortical and subcortical structures each provide distinct contributions to auditory pattern learning, but that cortical sensitivity to stimulus patterns likely precedes subcortical sensitivity.

Multimodal MRI Analysis of Brain Metabolism in Maintenance Hemodialysis Patients Based on Cognitive Computing

This paper investigates cognitive computation of brain metabolism in maintenance hemodialysis patients with multimodal MRI therapy assessment. This paper constructs a cross-individual emotion recognition method using dynamic sample entropy pattern learning. The cross-individual emotion recognition was carried out on subjects using the EEG emotion dataset SEED. The experimental results show that the proposed dynamic sample entropy-based pattern learning has better performance in cross-individual emotion recognition and exhibits better generalization and generalization ability when compared with the results of existing related studies. The constructed cognitive computing method for cross-individual emotion state recognition achieves optimization and innovation of EEG emotion pattern recognition, which can effectively predict people’s mental emotion state from EEG signals. We also explore the value of diffusion-weighted magnetic resonance imaging and dynamic enhanced magnetic resonance imaging-based volumetric measurements in assessing the efficacy of neoadjuvant therapy in maintenance hemodialysis patients. We analyze and compare the results of different studies to find the best multimodal MRI to assess the efficacy of neoadjuvant therapy in maintenance hemodialysis patients. The use of ADC value growth rates to assess neoadjuvant efficacy provides the best diagnostic efficacy and allows the screening of patients who respond well to neoadjuvant therapy while avoiding the impact of two different b-value combinations commonly used to assess neoadjuvant efficacy.

Tracking Changes of Hidden Food: Spatial Pattern Learning in Two Macaw Species

Food availability may vary spatially and temporally within an environment. Efficiency in locating alternative food sources using spatial information (e.g., distribution patterns) may vary according to a species’ diet and habitat specialisation. Hypothetically, more generalist species would learn faster than more specialist species due to being more explorative when changes occur. We tested this hypothesis in two closely related macaw species, differing in their degree of diet and habitat specialisation; the more generalist Great Green Macaw and the more specialist Blue-throated Macaw. We examined their spatial pattern learning performance under predictable temporal and spatial change, using a ‘poke box’ that contained hidden food placed within wells. Each week, the rewarded wells formed two patterns (A and B), which were changed on a mid-week schedule. We found that the two patterns varied in their difficulty. We also found that the more generalist Great Green Macaws took fewer trials to learn the easier pattern and made more mean correct responses in the difficult pattern than the more specialist Blue-throated Macaws, thus supporting our hypothesis. The better learning performance of the Great Green Macaws may be explained by more exploration and trading-off accuracy for speed. These results suggest how variation in diet and habitat specialisation may relate to a species’ ability to adapt to spatial variation in food availability.